mirror of https://github.com/open-mmlab/mmpose
9.3 KiB
9.3 KiB
HRNet (CVPR'2019)
@inproceedings{sun2019deep,
title={Deep high-resolution representation learning for human pose estimation},
author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={5693--5703},
year={2019}
}
COCO (ECCV'2014)
@inproceedings{lin2014microsoft,
title={Microsoft coco: Common objects in context},
author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
booktitle={European conference on computer vision},
pages={740--755},
year={2014},
organization={Springer}
}
Human-Art (CVPR'2023)
@inproceedings{ju2023humanart,
title={Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes},
author={Ju, Xuan and Zeng, Ailing and Jianan, Wang and Qiang, Xu and Lei, Zhang},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),
year={2023}}
Results on Human-Art validation dataset with detector having human AP of 56.2 on Human-Art validation dataset
With classic decoder
Arch | Input Size | AP | AP50 | AP75 | AR | AR50 | ckpt | log |
---|---|---|---|---|---|---|---|---|
pose_hrnet_w32-coco | 256x192 | 0.252 | 0.397 | 0.255 | 0.321 | 0.485 | ckpt | log |
pose_hrnet_w32-humanart-coco | 256x192 | 0.399 | 0.545 | 0.420 | 0.466 | 0.613 | ckpt | log |
pose_hrnet_w48-coco | 256x192 | 0.271 | 0.413 | 0.277 | 0.339 | 0.499 | ckpt | log |
pose_hrnet_w48-humanart-coco | 256x192 | 0.417 | 0.553 | 0.442 | 0.481 | 0.617 | ckpt | log |
Results on Human-Art validation dataset with ground-truth bounding-box
With classic decoder
Arch | Input Size | AP | AP50 | AP75 | AR | AR50 | ckpt | log |
---|---|---|---|---|---|---|---|---|
pose_hrnet_w32-coco | 256x192 | 0.533 | 0.771 | 0.562 | 0.574 | 0.792 | ckpt | log |
pose_hrnet_w32-humanart-coco | 256x192 | 0.754 | 0.906 | 0.812 | 0.783 | 0.916 | ckpt | log |
pose_hrnet_w48-coco | 256x192 | 0.557 | 0.782 | 0.593 | 0.595 | 0.804 | ckpt | log |
pose_hrnet_w48-humanart-coco | 256x192 | 0.769 | 0.906 | 0.825 | 0.796 | 0.919 | ckpt | log |
Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset
With classic decoder
Arch | Input Size | AP | AP50 | AP75 | AR | AR50 | ckpt | log |
---|---|---|---|---|---|---|---|---|
pose_hrnet_w32-coco | 256x192 | 0.749 | 0.906 | 0.821 | 0.804 | 0.945 | ckpt | log |
pose_hrnet_w32-humanart-coco | 256x192 | 0.741 | 0.902 | 0.814 | 0.795 | 0.941 | ckpt | log |
pose_hrnet_w48-coco | 256x192 | 0.756 | 0.908 | 0.826 | 0.809 | 0.945 | ckpt | log |
pose_hrnet_w48-humanart-coco | 256x192 | 0.751 | 0.905 | 0.822 | 0.805 | 0.943 | ckpt | log |